analysis

A company-analysis tool that examines financial statements, valuation, ownership, and business outlook across 22 analysis areas.

In plain words
What is it for?
Use it to review profitability, cash flow, growth, stability, efficiency, valuation, ownership structure, and company-specific economic risks.
Why use it?
It helps turn raw company figures into answers about how the business makes money, whether growth is sustainable, and whether its market price seems reasonable.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/eddmpython/dartlab/analysis
Any agent
npx skills add eddmpython/dartlab --skill analysis
Clone the repo
git clone --depth 1 https://github.com/eddmpython/dartlab

Made for: Claude Code, Codex.

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,070 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.07070
Opus 5 $0.00000 $0.03535
Sonnet 5 $0.00000 $0.01414
Haiku 4.5 $0.00000 $0.00707

Measured yesterday against content hash 72bea17eabe4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analysis scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/dartlab/skills/specs/engines/analysis/SKILL.md · 388 lines

How it starts

The opening of the file, as written. The whole thing — 388 lines — stays where its author put it; the contents beside it link to each section on GitHub.

엔진 역할

analysis는 단일 기업을 재무제표의 인과 구조로 읽는 L2 엔진이다. 이 엔진은 매출과 비용, 자산과 부채, 현금흐름, 수익성, 성장성, 안정성, 효율성, 가치평가, 지배구조, 전망 신호를 축별로 계산하고 해석한다.

담당하는 질문은 "이 회사는 무엇으로 돈을 벌고, 그 돈은 진짜 현금으로 남고, 그 성장은 지속 가능하며, 지금 가격은 어느 정도인가"이다. 시장 전체 매크로 전망은 engines.macro, 신용등급/부도위험 중심 평가는 engines.credit, 여러 종목을 찾는 작업은 engines.scan, 최종 보고서 조합은 engines.story가 담당한다.

공개 호출 방식

기본 호출은 Company.analysis()이다. 축을 모르면 먼저 가이드를 보고, 축을 알면 group과 axis를 지정한다.

import dartlab

c = dartlab.Company("005930")

# 1. 전체 분석 축 가이드
guide = c.analysis()

# 2. 그룹 가이드 또는 그룹 내 계산 항목 확인
financial = c.analysis("financial")

# 3. 특정 분석 축 실행
profitability = c.analysis("financial", "수익성")
cashflow = c.analysis("financial", "현금흐름")
valuation = c.analysis("valuation", "가치평가")
forecast = c.analysis("forecast", "매출전망")

# 4. 축 이름만으로 실행 가능한 경우
profitability = c.analysis("수익성")

# 5. 모듈 함수형 호출 — Company 객체는 company= keyword (positional 3 번째 안 받음)
profitability = dartlab.analysis("financial", "수익성", company=c)
valuation = dartlab.analysis("valuation", "가치평가", company=c)
# stockCode 단독 호출도 가능
profitability = dartlab.analysis("financial", "수익성", stockCode="005930")

노트북이나 MCP에서 사람이 따라 할 때도 같은 순서로 쓴다. 먼저 c.analysis()로 가능한 축을 확인하고, 그 다음 실제 축을 호출한다.

강행 호출 룰 (agent 답변 품질 회귀 차단)

22 axis 질문 (수익성·밸류에이션·안정성·효율성·종합평가·이익품질·자본배분·성장성 등) 에서 다음 4 룰은 강행이다 — 위반 시 refs=0 회귀로 답변 품질 65 점 이하 하락.

  1. 1 차 도구는 EngineCall 강제. axis 명이 질문에 있거나 22 axis 가이드 표에 매칭되면 EngineCall(apiRef="Company.analysis", args={...}) 또는 EngineCall(apiRef="Company.panel", args={...}) 가 첫 호출. RunPython 직접 ratio 계산은 engine 호출 결과가 부재할 때만 fallback — 처음부터 raw 계산 금지.

    이유: EngineCall 결과 dict 는 @tagConfidence 데코레이터로 tableRef·valueRef·dateRef·executionRef 자동 발급. RunPython 은 raw eval — refs 0 발급. 답변 본문 인용 가치체인이 깨진다.

  2. 본문 안 모든 숫자에는 inline ref 표기 필수. 형식: 13.07% [ref:vr_...] 또는 [tableRef: tr_...]. ref 없는 숫자는 답변에 적지 않거나 "EngineCall 재시도 필요" 명시.

  3. dataAsOf 확인 → 답변 첫 줄 명시. 결과 dict 의 dataAsOf 가 stale (3 분기 이상 전) 이면 "현재 시점 단정 X — dataAsOf 기준" 명시.

Read the full file on GitHub · 388 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 388 lines · 0 tokens per session scan A 72bea17eabe4

Subscribe to this mod's changes

analysis is a skill published in the GitHub repository eddmpython/dartlab (209 stars, last pushed 10d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 7,070 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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